How does Atlas handle a scikit-learn stack trace from an older build?

Atlas validates each frame's offset against the current file. If `atlas read` reports "Offset is out of range for this file", it indicates the trace is from a different build. Re-read the file from the top before trusting any line number.

Can Atlas help me find the source of a `ValueError` in a scikit-learn Pipeline?

Yes, after pasting the stack trace, use `atlas grep` to search for the specific `ValueError` message. This helps locate the exact line in scikit-learn's source, like `sklearn/pipeline.py`, where the error is raised, providing more context than just the top frame.

How does Atlas ensure my scikit-learn code changes are safe?

Atlas operates with permission-gated tool calls and presents a unified diff for every file edit, such as to `sklearn/preprocessing/_data.py`, for your explicit approval before writing. It also drafts plans in a read-only agent first.

Can Atlas run `pytest` for my scikit-learn project?

Yes, Atlas can run `pytest` behind a permission prompt. This allows you to validate your fixes and regression tests directly within the Atlas terminal, ensuring your scikit-learn codebase remains stable.

What if I need to find all usages of a specific scikit-learn utility function?

Atlas's `lsp` tool, specifically `atlas lsp findReferences`, can accurately locate all call sites of a function within your scikit-learn project. This is invaluable for understanding data flow and impact analysis.

Does Atlas support scikit-learn's `pyproject.toml` for package management?

Yes, Atlas runs in projects with a `pyproject.toml` that pins scikit-learn. It integrates with `uv` as the package manager, ensuring your environment is correctly set up for development and testing.

How does Atlas help maintain scikit-learn code style after a fix?

After applying a fix with `atlas edit`, you can ask Atlas to apply `ruff format` to the generated diff. This ensures that your changes adhere to the project's formatting standards, maintaining code consistency.

Trace a scikit-learn Runtime Bug from a Stack Trace with Atlas in 2026

Atlas empowers scikit-learn developers in 2026 to trace runtime bugs from production stack traces directly to the responsible line and apply a fix, all without attaching a debugger. By integrating with your existing `pytest`, `uv`, and `ruff format` toolchain, Atlas streamlines the debugging process, ensuring rapid resolution of critical issues.

How Atlas Reads a scikit-learn Production Stack Trace

To initiate a bug trace in scikit-learn, you paste the production stack trace directly into the Atlas terminal, which then uses its `read` tool to examine each frame. This process involves Atlas reading each file at its reported offset, ensuring that the 10 to 20 lines of context are relevant to your current codebase.

Atlas's `read` tool is designed to consume a stack trace, which is fundamentally a list of `file:line` pairs. When you paste a scikit-learn stack trace, Atlas processes each frame, such as `sklearn/preprocessing/_data.py:456` or `sklearn/ensemble/_forest.py:789`. A critical safety feature is Atlas's validation of these offsets against the current file content. If `atlas read` reports "Offset <n> is out of range for this file", it immediately signals that the trace originated from an older build. In such cases, Atlas advises re-reading the file from the top before trusting any line number, preventing misdiagnosis based on outdated code. This ensures that the initial investigation is always grounded in the exact version of the scikit-learn codebase you are working with.

Finding the Root Cause: Grepping for scikit-learn Error Messages

After Atlas reads the initial stack trace, the next step in diagnosing a scikit-learn runtime bug is to `grep` for the specific error message string. This often provides more insight than the top frame alone, helping to pinpoint the exact location where the error message is constructed within the scikit-learn source, typically within 3 to 5 relevant files.

While the top frame of a stack trace indicates where an error surfaced, the actual cause often lies deeper, where the error message itself is generated. Atlas leverages its `grep` tool to search your scikit-learn project for the exact error message string. For instance, if a `ValueError` related to input shapes appears, Atlas can quickly locate the `raise ValueError(...)` statement within files like `sklearn/utils/validation.py` or `sklearn/linear_model/_base.py`. This approach is particularly effective in large codebases like scikit-learn, where helper functions and validation logic can abstract the true origin of an issue. By finding where the error message is constructed, Atlas helps you understand the conditions that trigger the bug, providing a more informative starting point for a fix.

Identifying Callers with Atlas's LSP Tool in scikit-learn

Once the error message's construction point is identified, Atlas uses its `lsp` tool's `findReferences` operation to discover all callers of the failing function within your scikit-learn project. This allows you to trace back how bad input might reach the problematic code, often revealing 5 to 15 potential call sites that need examination.

Understanding how an invalid input propagates to a failing function is crucial for a robust fix. Atlas integrates with Language Server Protocol (LSP) servers to provide advanced code intelligence. Using `atlas lsp findReferences` on the function identified by the `grep` step, Atlas can list every location in your scikit-learn codebase that calls this function. For example, if a validation function in `sklearn/utils/validation.py` is failing, Atlas can show all estimators or utility functions that invoke it. This comprehensive view helps you identify all possible paths through which incorrect data or parameters could lead to the bug, ensuring that your fix addresses the root cause rather than just a symptom. Atlas's AST-based indexing, powered by tree-sitter, ensures highly accurate and context-aware reference finding, unlike simpler line-based searches.

Applying and Testing Fixes in scikit-learn with Atlas

After identifying the problematic code and its callers, Atlas facilitates applying the fix using its `edit` tool and then helps you add a regression test. This ensures the bug cannot recur silently, with Atlas providing a unified diff for approval before writing changes and running `pytest` to validate the fix, typically involving 1 to 2 new test cases.

Atlas's `edit` tool allows you to propose and apply code changes directly within the terminal. For a scikit-learn bug, this might involve modifying input validation in an estimator's `fit` method, adjusting a `ColumnTransformer` definition, or correcting a `Pipeline` step. Before any changes are written to disk, Atlas computes a unified diff and surfaces it for your approval, giving you full control over the modifications. After applying the fix, Atlas guides you to add a regression test. You can ask Atlas to run `pytest` behind a permission prompt to verify the fix and ensure no new issues are introduced. For example, you might add a test case to `sklearn/tests/test_estimator.py` that specifically triggers the original bug, now expecting it to pass. Atlas can also apply `ruff format` to the diff, ensuring your changes adhere to scikit-learn's coding standards.

Atlas's Safety and Review Mechanisms for scikit-learn Development

Atlas incorporates robust safety and review mechanisms throughout the bug-fixing workflow, ensuring that all actions, from code indexing to file edits, are permission-gated. Every Atlas tool call is subject to allow, ask, or deny rules, providing scikit-learn developers with complete control over the AI agent's operations, typically involving 3 to 4 explicit approval steps.

Atlas is designed with developer control at its core. When working on a scikit-learn project, Atlas first drafts a plan in a read-only plan agent and asks for your approval before switching to a build agent to execute any actions. All tool calls, such as `atlas read`, `atlas grep`, `atlas lsp`, or `atlas edit`, are permission-gated. This means you explicitly approve Atlas's use of these tools, preventing unintended operations. For code modifications, Atlas always presents a unified diff for your review and approval before writing changes to files like `sklearn/preprocessing/_data.py` or `pyproject.toml`. Furthermore, Atlas can snapshot file changes as git patches, allowing edits to be easily diffed and rolled back if necessary. This layered approach to safety ensures that Atlas acts as a powerful assistant, not an autonomous agent, maintaining your oversight throughout the scikit-learn development process.

Step by step

01Paste the scikit-learn production stack trace into Atlas and use `atlas read` to examine each frame, such as `sklearn/ensemble/_forest.py:123`.
02If `atlas read` reports "Offset <n> is out of range for this file", re-read the file from the top before trusting any line number, indicating an older build trace.
03Use `atlas grep` to search for the exact error message string within your scikit-learn codebase to find where it is constructed, e.g., in `sklearn/utils/validation.py`.
04Employ `atlas lsp findReferences` on the failing function to identify all callers that could supply the bad input, tracing the data flow through scikit-learn components.
05Use `atlas edit` to apply the necessary fix to the scikit-learn source code, such as modifying a `fit` method or a `ColumnTransformer` definition.
06Add a regression test case to your scikit-learn project, for example in `sklearn/tests/test_estimator.py`, to prevent the bug from recurring silently.
07Ask Atlas to run `pytest` behind a permission prompt to validate your fix and ensure all scikit-learn tests pass.
08Have Atlas apply `ruff format` to the generated diff to ensure code style consistency before committing.

Frequently asked questions

How does Atlas handle a scikit-learn stack trace from an older build?: Atlas validates each frame's offset against the current file. If `atlas read` reports "Offset <n> is out of range for this file", it indicates the trace is from a different build. Re-read the file from the top before trusting any line number.
Can Atlas help me find the source of a `ValueError` in a scikit-learn Pipeline?: Yes, after pasting the stack trace, use `atlas grep` to search for the specific `ValueError` message. This helps locate the exact line in scikit-learn's source, like `sklearn/pipeline.py`, where the error is raised, providing more context than just the top frame.
How does Atlas ensure my scikit-learn code changes are safe?: Atlas operates with permission-gated tool calls and presents a unified diff for every file edit, such as to `sklearn/preprocessing/_data.py`, for your explicit approval before writing. It also drafts plans in a read-only agent first.
Can Atlas run `pytest` for my scikit-learn project?: Yes, Atlas can run `pytest` behind a permission prompt. This allows you to validate your fixes and regression tests directly within the Atlas terminal, ensuring your scikit-learn codebase remains stable.
What if I need to find all usages of a specific scikit-learn utility function?: Atlas's `lsp` tool, specifically `atlas lsp findReferences`, can accurately locate all call sites of a function within your scikit-learn project. This is invaluable for understanding data flow and impact analysis.
Does Atlas support scikit-learn's `pyproject.toml` for package management?: Yes, Atlas runs in projects with a `pyproject.toml` that pins scikit-learn. It integrates with `uv` as the package manager, ensuring your environment is correctly set up for development and testing.
How does Atlas help maintain scikit-learn code style after a fix?: After applying a fix with `atlas edit`, you can ask Atlas to apply `ruff format` to the generated diff. This ensures that your changes adhere to the project's formatting standards, maintaining code consistency.

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